{"query": "Which citations in the paper discuss the development and release of Llama 2?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Zellers et al.\",\n    \"year\": 2019,\n    \"desc\": \"argued that open releases promote transparency and allow more people to access AI tools, democratizing the technology and decentralizing AI expertise\"\n  },\n  {\n    \"author\": \"Kaplan et al.\",\n    \"year\": 2020,\n    \"desc\": \"discussed the scaling laws of Large Language Models\"\n  },\n  {\n    \"author\": \"Touvron et al.\",\n    \"year\": 2023,\n    \"desc\": \"recognized Llama for its focus on computational efficiency during inference\"\n  }\n]\n}"}
{"query": "Which citations are mentioned in the section on RLHF Results?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Gilardi et al.\",\n    \"year\": 2023,\n    \"desc\": \"\"\n  },\n  {\n    \"author\": \"Huang et al.\",\n    \"year\": 2023,\n    \"desc\": \"\"\n  }\n]\n}"}
{"query": "Which citations provide additional details for fine-tuning in the paper \"Llama 2: Open Foundation and Fine-Tuned Chat Models\"?", "response": "{\n\"citations\": [\n{\n\"author\": \"\",\n\"year\": 47,\n\"desc\": \"A.3 Additional Details for Fine-tuning\"\n}\n]\n}"}
{"query": "Which citations are related to the development of pretrained LLMs?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Yuntian Deng, Anton Bakhtin, Myle Ott, Arthur Szlam, and Marc\u2019Aurelio Ranzato\",\n    \"year\": 2019,\n    \"desc\": \"Residual energy-based models for text generation. In International Conference on Learning Representations\"\n  },\n  {\n    \"author\": \"Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P Bosma, Zongwei Zhou, Tao Wang, Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc Le, Yonghui Wu, Zhifeng Chen, and Claire Cui\",\n    \"year\": 2022,\n    \"desc\": \"GLaM: Efficient scaling of language models with mixture-of-experts. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 5547\u20135569. PMLR, 17\u201323 Jul 2022. URL https://proceedings.mlr.press/v162/du22c.html\"\n  }\n]\n}"}
{"query": "Which citations discuss the potential risks associated with the use of Llama 2?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Bender et al.\",\n    \"year\": 2021,\n    \"desc\": \"\"\n  },\n  {\n    \"author\": \"Weidinger et al.\",\n    \"year\": 2021,\n    \"desc\": \"\"\n  },\n  {\n    \"author\": \"Solaiman et al.\",\n    \"year\": 2023,\n    \"desc\": \"\"\n  }\n]\n}"}
{"query": "Which citation refers to the pretraining approach used in the creation of Llama 2 models?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Touvron et al.\",\n    \"year\": 2023,\n    \"desc\": \"using an optimized auto-regressive transformer\"\n  }\n]\n}"}
{"query": "Which citation is referred to in the context of the tokenizer used in Llama 1 and 2?", "response": "{\n\"citations\": [\n{\n\"author\": \"Irene Solaiman, Zeerak Talat, William Agnew, Lama Ahmad, Dylan Baker, Su Lin Blodgett, Hal Daum\u00e9 III, Jesse Dodge, Ellie Evans, Sara Hooker\",\n\"year\": 2023,\n\"desc\": \"Evaluating the social impact of generative ai systems in systems and society. arXiv preprint arXiv:2306.05949\"\n},\n{\n\"author\": \"Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano\",\n\"year\": 2020,\n\"desc\": \"Learning to summarize from human feedback. In NeurIPS\"\n},\n{\n\"author\": \"Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu\",\n\"year\": 2022,\n\"desc\": \"Roformer: Enhanced transformer with rotary position embedding\"\n},\n{\n\"author\": \"Mirac Suzgun, Nathan Scales, Nathanael Sch\u00e4rli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou\",\n\"year\": 2022,\n\"desc\": \"Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261\"\n},\n{\n\"author\": \"Gabriel Synnaeve, Jonas Gehring, Zeming Lin, Daniel Haziza, Nicolas Usunier, Danielle Rothermel, Vegard Mella, Da Ju, Nicolas Carion, Laura Gustafson\",\n\"year\": 2019,\n\"desc\": \"Growing up together: Structured exploration for large action spaces\"\n},\n{\n\"author\": \"Yarden Tal, Inbal Magar, and Roy Schwartz\",\n\"year\": 2022,\n\"desc\": \"Fewer errors, but more stereotypes? the effect of model size on gender bias. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 112\u2013120, Seattle, Washington, July 2022. Association for Computational Linguistics. doi: 10.18653/v1/2022.gebnlp-1.13. URL https://aclanthology.org/2022.gebnlp-1.13\"\n},\n{\n\"author\": \"Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant\",\n\"year\": 2018,\n\"desc\": \"Commonsenseqa: A question answering challenge targeting commonsense knowledge. arXiv preprint arXiv:1811.00937\"\n},\n{\n\"author\": \"Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto\",\n\"year\": 2023,\n\"desc\": \"Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca\"\n},\n{\n\"author\": \"Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic\",\n\"year\": 2022,\n\"desc\": \"Galactica: A large language model for science. arXiv preprint arXiv:2211.09085\"\n},\n{\n\"author\": \"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Faisal Azhar, Aur\u2019elien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample\",\n\"year\": 2023,\n\"desc\": \"Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971\"\n},\n{\n\"author\": \"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin\",\n\"year\": 2017,\n\"desc\": \"Attention is all you need\"\n},\n{\n\"author\": \"Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Micha\u00ebl Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev\",\n\"year\": 2019,\n\"desc\": \"Grandmaster level in starcraft ii using multi-agent reinforcement learning. Nature, 575(7782):350\u2013354\"\n}\n]\n}"}
{"query": "Which citation discusses the carbon output related to the production of AI hardware?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Gupta et al.\",\n    \"year\": 2022,\n    \"desc\": \"the carbon output related to the production of AI hardware\"\n  }\n]\n}"}
{"query": "Which citation is associated with the MMLU benchmark?", "response": "{\n\"citations\": []\n}"}
{"query": "Which citations provide the results for GPT-3.5 and GPT-4 in the academic benchmarks comparison?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"OpenAI\",\n    \"year\": 2023,\n    \"desc\": \"Results for GPT-3.5 and GPT-4\"\n  }\n]\n}"}
{"query": "Which citations are referred to in the section on Supervised Fine-Tuning?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Chung et al.\",\n    \"year\": 2022,\n    \"desc\": \"publicly available instruction tuning data\"\n  },\n  {\n    \"author\": \"Touvron et al.\",\n    \"year\": 2023,\n    \"desc\": \"utilized previously\"\n  },\n  {\n    \"author\": \"Zhou et al.\",\n    \"year\": 2023,\n    \"desc\": \"finds that a limited set of clean instruction-tuning data can be sufficient to reach a high level of quality\"\n  }\n]\n}"}
{"query": "Which citation discusses the issue of hyper-specialization in reward model accuracy?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Ethayarajh et al.\",\n    \"year\": 2022,\n    \"desc\": \"SteamSHP-XL based on FLAN-T5-xl\"\n  },\n  {\n    \"author\": \"K\u00f6pf et al.\",\n    \"year\": 2023,\n    \"desc\": \"Open Assistant reward model based on DeBERTa V3 Large\"\n  },\n  {\n    \"author\": \"He et al.\",\n    \"year\": 2020,\n    \"desc\": \"DeBERTa V3 Large\"\n  }\n]\n}"}
{"query": "Which citation is referred to in the discussion about the binary ranking loss used in the training of the reward model?", "response": "{\n\"citations\": [\n{\n\"author\": \"Ouyang\",\n\"year\": 2022,\n\"desc\": \"\"\n}\n]\n}"}
{"query": "What citations are related to the use of open-source preference datasets in the study?", "response": "{\n\"citations\": [\n{\n\"author\": \"Huggingface h4 stack\",\n\"year\": 2023,\n\"desc\": \"exchange preference dataset. URL https://huggingface.co/datasets/HuggingFaceH4/ stack-exchange-preferences.\"\n}\n]\n}"}
{"query": "Which citation corresponds to the SteamSHP-XL model?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Ethayarajh et al.\",\n    \"year\": 2022,\n    \"desc\": \"SteamSHP-XL based on FLAN-T5-xl\"\n  }\n]\n}"}
{"query": "Which citations are related to the scaling trends for the reward model?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Schulman et al.\",\n    \"year\": 2017,\n    \"desc\": \"Proximal Policy Optimization (PPO), the standard in RLHF literature.\"\n  },\n  {\n    \"author\": \"Bai et al.\",\n    \"year\": 2022,\n    \"desc\": \"Rejection Sampling fine-tuning. We sample K outputs from the model and select the best candidate with our reward\"\n  },\n  {\n    \"author\": \"Deng et al.\",\n    \"year\": 2019,\n    \"desc\": \"The same re-ranking strategy for LLMs was also proposed, where the reward is seen as an energy function.\"\n  }\n]\n}"}
{"query": "Which citation discusses the concept of Rejection Sampling in the context of RL algorithms?", "response": "{\n\"citations\": [\n{\n\"author\": \"Scialom et al.\",\n\"year\": 2020,\n\"desc\": \"the highest reward score is considered the new gold standard. Similar to Scialom et al. (2020a), we then fine-tune our model on the new set of ranked samples, reinforcing the reward.\"\n}\n]\n}"}
{"query": "Which citation is associated with the RL scheme used to train the language model in this study?", "response": "{\n\"citations\": [\n{\n\"author\": \"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Nee-lakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei\",\n\"year\": 2020,\n\"desc\": \"Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 1877\u20131901. Curran Associates, Inc.\"\n},\n{\n\"author\": \"Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba\",\n\"year\": 2021,\n\"desc\": \"Evaluating large language models trained on code\"\n},\n{\n\"author\": \"Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing\",\n\"year\": 2023,\n\"desc\": \"Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality\"\n},\n{\n\"author\": \"Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer\",\n\"year\": 2018,\n\"desc\": \"Quac: Question answering in context. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing\"\n}\n]\n}"}
{"query": "Which citation is associated with the FSDP method used in the training of the models?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Zhao et al.\",\n    \"year\": 2023,\n    \"desc\": \"FSDP method used in training of models\"\n  }\n]\n}"}
{"query": "Which citations provide information about the challenges of evaluating LLMs?", "response": "{\n\"citations\": [\n  {\n    \"author\": \"Lin et al.\",\n    \"year\": 2021,\n    \"desc\": \"Used for LLM hallucinations to measure whether a language model is truthful in generating answers to questions while being informative at the same time.\"\n  },\n  {\n    \"author\": \"Ouyang et al.\",\n    \"year\": 2022,\n    \"desc\": \"InstructGPT used for the QA prompt.\"\n  },\n  {\n    \"author\": \"Hartvigsen et al.\",\n    \"year\": 2022,\n    \"desc\": \"ToxiGen, a dataset that contains implicitly toxic and benign sentences mentioning 13 minority groups.\"\n  },\n  {\n    \"author\": \"Hosseini et al.\",\n    \"year\": 2023,\n    \"desc\": \"A revised version of the ToxiGen dataset that reduces noise by filtering out prompts for which annotators disagree on the target demographic group.\"\n  },\n  {\n    \"author\": \"Liu et al.\",\n    \"year\": 2019,\n    \"desc\": \"RoBERTa used to measure the toxicity of generations of each of the LLMs.\"\n  },\n  {\n    \"author\": \"Dhamala et al.\",\n    \"year\": 2021,\n    \"desc\": \"BOLD, a large-scale bias benchmark that comprises English Wikipedia prompts spanning five domains.\"\n  },\n  {\n    \"author\": \"Hutto and Gilbert\",\n    \"year\": 2014,\n    \"desc\": \"VADER used to evaluate the sentiments conveyed by the combination of prompt prefix and model generation.\"\n  }\n]\n}"}
{"query": "Which citations are related to the progression of SFT and RLHF versions?", "response": "{\n  \"citations\": [\n    {\n      \"author\": \"Gilardi et al.\",\n      \"year\": 2023,\n      \"desc\": \"Documented the superior writing abilities of LLMs, as manifested in surpassing human annotators in certain tasks, are fundamentally driven by RLHF\"\n    },\n    {\n      \"author\": \"Huang et al.\",\n      \"year\": 2023,\n      \"desc\": \"Supported the findings of Gilardi et al. on the effectiveness of RLHF in driving the superior writing abilities of LLMs\"\n    }\n  ]\n}"}
{"query": "Which citations are referred to in the discussion about safety investigations into pretraining data and pretrained models?", "response": "{\n\"citations\": [\n{\n\"author\": \"Emily Dinan, Gavin Abercrombie, A Stevie Bergman, Shannon Spruit, Dirk Hovy, Y-Lan Boureau, and Verena Rieser\",\n\"year\": 2021,\n\"desc\": \"Anticipating safety issues in e2e conversational ai: Framework and tooling. arXiv preprint arXiv:2107.03451\"\n},\n{\n\"author\": \"Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al.\",\n\"year\": null,\n\"desc\": \"Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned.\"\n}\n]\n}"}
